With the rise of hybrid work and flexible office arrangements, occupancy in commercial buildings fluctuates more than ever. Traditional schedules no longer reflect actual usage, leading to energy waste. Occupancy analytics leverages data from sensors and booking systems to adjust HVAC, lighting and space management dynamically. When combined with digital twins and machine-learning optimisation, occupancy analytics can deliver up to 30% energy reduction while enhancing comfort and productivity.
This article explores how offices and smart workplaces achieve significant energy savings through occupancy analytics. We draw on Neuron case studies and link to related resources such as Energy & Carbon Saving for Singapore’s Green Buildings, Smart Offices in Singapore and Energy & Carbon Savings: ROI for Developers, Operators, and Investors.
What is occupancy analytics?
Occupancy analytics involves collecting and analysing data on how spaces are used. Data sources include:
IoT sensors. Motion detectors, desk sensors, people counters and CO₂ sensors reveal when spaces are occupied and how many people are present.
Booking systems. Calendar integrations show scheduled meetings and desk reservations.
Access control and Wi-Fi. Badge swipes and network connections provide anonymised insights into occupancy patterns.
Environmental sensors. Temperature, humidity and air quality data provide context for comfort and ventilation needs.
This data feeds into an IoT hub that harmonises and analyses it. Real-time dashboards and machine-learning models then adjust building systems accordingly.
Energy savings mechanisms
Dynamic HVAC control
Heating, ventilation and air-conditioning (HVAC) systems are major energy consumers. By linking occupancy data to HVAC setpoints and schedules, buildings avoid conditioning empty spaces. Spaces can be pre-conditioned when occupancy is predicted to rise and set back when vacant. All Seasons Place replaced an obsolete BMS controller with a smart edge device, achieving 8–10% energy savings in HVAC operations. Combining occupancy analytics with machine-learning optimisation of the chiller plant, as implemented at One Taikoo Place, can push savings closer to 34%.
Lighting and plug load management
Lighting controls adjust brightness or switch off lights in unoccupied areas. Smart plugs disconnect devices in empty workstations. Occupancy sensors in meeting rooms turn off audiovisual equipment when not in use. Since lighting can account for 20–25%? (data from US Energy Information Administration and ENERGY STAR) of office energy use, these measures contribute substantially to the overall reduction.
Space optimisation and consolidation
Analytics reveal under-utilised floors or departments. Companies can consolidate teams onto fewer floors, shutting down HVAC and lighting in unused areas. Flexible work policies (hot-desking, remote work) become more efficient when guided by occupancy data. This approach reduces base building energy consumption and may enable subleasing unused space. With Digital Twin‘s free furniture creation and workspace editing tools, you can effortlessly make any changes to your office environment
Ventilation and indoor air quality
Demand-controlled ventilation adjusts fresh air intake based on CO₂ levels and occupancy. This ensures healthy air quality while reducing fan energy. High indoor air quality improves productivity and reduces sick-building syndrome.
Machine-learning optimisation
Advanced models forecast occupancy patterns by analysing historical data, public holidays, weather and booking information. They adjust chiller setpoints, fan speeds and pump operations in anticipation of demand. In One Taikoo Place, machine-learning-assisted optimisation and digital twins played a significant role in energy savings.
Implementation roadmap
Assess current occupancy patterns.
Conduct surveys and install pilot sensors on a few floors to gather baseline data.
Deploy an IoT hub and sensors.
Install occupancy sensors, CO₂ sensors and smart plugs. Integrate existing BMS and booking systems via open APIs.
Develop analytics and dashboards.
Visualise real-time occupancy, energy consumption and comfort metrics. Identify areas with high energy use relative to occupancy.
Automate HVAC and lighting control.
Use occupancy data to control AHUs, VAV, lighting zones and plug loads. Implement demand-controlled ventilation.
Pilot machine-learning models.
Forecast occupancy and energy demand. Optimise chiller plant operation and adapt strategies based on feedback.
Review and iterate.
Use occupant feedback and performance data to refine control algorithms. Expand the system to more floors or buildings.
Case examples
All Seasons Place
Immediate energy savings of 8–10% were achieved by upgrading the BMS and implementing occupancy-driven HVAC control.
One Taikoo Place
Machine-learning optimisation of the chiller plant delivered around 10% energy savings. When combined with occupancy analytics and lighting controls, total savings can approach 34% across the building.
Six Pacific Place and RDCC
Unified data platforms and digital twins enabled predictive maintenance and performance benchmarking. These frameworks support large-scale deployment of occupancy analytics across portfolios.
Conclusion
Occupancy analytics empowers smart workplaces to achieve significant energy reductions, often up to 30%, by aligning building operations with actual usage. It requires a robust IoT platform (link to IoT) to collect and analyse data, advanced controls to respond in real time and a commitment to continuous improvement. Neuron’s projects, including All Seasons Place and One Taikoo Place, demonstrate that combining occupancy data with machine-learning optimisation delivers measurable savings. As organisations adapt to hybrid work, investing in occupancy analytics will pay dividends in energy efficiency, occupant wellbeing and sustainability certifications.
For more guidance, explore Energy & Carbon Saving for Singapore’s Green Buildings and related clusters on Smart Offices, ROI for Energy & Carbon Savings and Public Organisations & Carbon Reporting.
FAQs
What data sources are used for occupancy analytics?
Typical sources include motion detectors, desk sensors, badge swipes, Wi-Fi connections, booking systems and CO₂ sensors. These data points provide insight into when and how spaces are used.
How is occupant privacy protected?
Data should be anonymised and aggregated. Sensors need not capture identifiable information. Clear policies, transparency and opt-in consent are essential to maintain trust.
Can occupancy analytics be applied in small offices?
Yes. Even small offices benefit from occupancy-based lighting and HVAC controls. Cloud-based IoT platforms make deployment cost-effective, and wireless sensors simplify installation.
What are the costs and payback period?
Costs include sensors, integration and analytics software. Savings from reduced energy bills often provide payback within 1–3 years, depending on building size and baseline efficiency. Rebates and green financing can shorten payback periods.
How does occupancy analytics interact with machine-learning optimisation?
Occupancy data enhances the accuracy of machine-learning models that predict energy demand. These models proactively adjust equipment operation, ensuring that energy savings do not compromise comfort or productivity.
